DRL aims to solve an MDP by taking suitable actions through an agent by interacting with the unknown environment to maximize the reward. In order to use DRL to solve the problem in (), we model our resource management problem as an MDP [1]}, which includes the DRL agent and the interactive environment shown in Fig. REF . Each V2V pair acts as an intelligent agent to make its own decision on the sub-band assignment and power allocation. All V2V links make their own decisions at each time slot. Since the goal of DRL is to learn the best policy via maximizing the total accumulated reward, three key elements, i.e., state, action, and rewards need to be first defined for our problem [2]}.
